扣带回前部
电休克疗法
精神分裂症(面向对象编程)
心理学
灰质
萧条(经济学)
基于体素的形态计量学
重性抑郁障碍
脑岛
磁共振成像
神经影像学
尾状核
神经科学
医学
精神科
白质
认知
放射科
经济
宏观经济学
作者
Hirotsugu Kawashima,Shimpei Yamasaki,Manabu Kubota,Masaaki Hazama,Yasutaka Fushimi,Jun Miyata,Toshiya Murai,Taro Suwa
标识
DOI:10.1016/j.nicl.2023.103429
摘要
Electroconvulsive therapy (ECT) is one of the most effective treatments for depression and schizophrenia, particularly in urgent or treatment-resistant cases. After ECT, regional gray matter volume (GMV) increases have been repeatedly reported both in depression and schizophrenia. However, the interpretation of these findings remains entangled because GMV changes do not necessarily correlate with treatment effects and may be influenced by the intervention itself. We hypothesized that the comparison of longitudinal magnetic resonance imaging data between the two diagnostic groups will provide clues to distinguish diagnosis-specific and transdiagnostic changes.Twenty-nine Japanese participants, including 18 inpatients with major depressive disorder and 11 with schizophrenia, underwent longitudinal voxel-based morphometry before and after ECT. We investigated GMV changes common to both diagnostic groups and those specific to each group. Moreover, we also evaluated potential associations between GMV changes and clinical improvement for each group.In both diagnostic groups, GMV increased in widespread areas after ECT, sharing common regions including: anterior temporal cortex; medial frontal and anterior cingulate cortex; insula; and caudate nucleus. In addition, we found a schizophrenia-specific GMV increase in a region including the left pregenual anterior cingulate cortex, with volume increase significantly correlating with clinical improvement.Transdiagnostic volume changes may represent the effects of the intervention itself and pathophysiological changes common to both groups. Conversely, diagnosis-specific volume changes are associated with treatment effects and may represent pathophysiology-specific impacts of ECT.
科研通智能强力驱动
Strongly Powered by AbleSci AI